One of the major challenges in wireless communication systems is to overcome interference caused by other users, such as when a mobile device in cellular systems receives interfering signals from multiple transmitters. Traditional schemes attempt to manage interference as noise or by orthogonalizing channel resources between different transmitters (base stations or access points) by assigning different frequency channels, time slots, or codes to different resources (e.g., FDMA/TDMA/CDMA). In addition, concurrent transmission techniques (interference alignment (IA)) have been proposed in which multiple senders jointly encode signals to multiple receivers so that interference is aligned and each receiver is able to decode its desired information. Interference alignment provides better performance than orthogonalization-based schemes by aligning the interference at a receiver coming from different sources in the least possible spatial dimensions to maximize the number of interference-free dimensions and hence, providing more degrees of freedom for signal transmission and improving the throughput performance. With interference alignment, a transmitter can partially or completely “align” its interference with unused dimensions of the primary terminals, thereby maximizing the interference-free space for the desired signal in an interference channel. For example, it has been shown that all the interference can be concentrated roughly into one half of the signal space at each receiver, leaving the other half available to the desired signal and free of interference. When considering sum capacity for n users in the high SNR regime, the sum capacity for each transmitter scaling as n/2 log(SNR) is achievable which is equivalent to n/2 degrees of freedom for the sum capacity for each transmitter. Moreover, for fixed SNR values, the sum capacity achieved by interference alignment has been shown to scale linearly with n. Interference alignment has also been considered in a Macro-cell scenario where multiple base stations (eNBs), each serving a rank-one user equipment (UE) device, collaborate with each other to decrease the effect of interference caused to each other's transmissions.
A significant challenge with existing interference alignment schemes is that they require perfect global channel knowledge about all channels in the network, which in turn imposes significant feedback overhead and coordination between nodes. In addition, existing interference alignment schemes are highly sensitive to channel estimation and quantization error, antenna configuration, and mobility. Accordingly, a need exists for improved methods, systems and devices for managing interference between network nodes to overcome the problems in the art, such as outlined above. Further limitations and disadvantages of conventional processes and technologies will become apparent to one of skill in the art after reviewing the remainder of the present application with reference to the drawings and detailed description which follow.